Many security systems require reliable personal identification or verification. Biometric technology overcomes many of the disadvantages of conventional identification and verification techniques, such as keys, ID cards, and passwords. Biometrics refers to an automatic recognition of individuals based on features representing physiological and/or behavioral characteristics.
A number of physiological features can be used as biometric cues, such as DNA samples, face topology, fingerprint minutia, hand geometry, handwriting style, iris appearance, retinal vein configuration, and speech spectrum. Among all these features, iris recognition has very high accuracy. The iris carries very distinctive information. Even the irises of identical twins are different.
Iris Localization
Typically, iris analysis begins with iris localization. One prior art method uses an integro-differential operator (IDO), Daugman, J. G., “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Volume 15, pp. 1148-1161, 1993, incorporated herein. The IDO locates the inner and outer boundaries of an iris using the following optimization,
                              max                      (                          r              ,                              x                0                            ,                              y                0                                      )                          ⁢                                                                      G                σ                            ⁡                              (                r                )                                      *                          ∂                              ∂                r                                      ⁢                                          ∮                                  r                  ,                                      x                    0                                    ,                                      y                    0                                                              ⁢                                                                    I                    ⁡                                          (                                              x                        ,                        y                                            )                                                                            2                    ⁢                    πr                                                  ⁢                                  ⅆ                  s                                                                                                  (        1        )            where I(x, y) is an image including an eye. The IDO searches over the image I(x, y) for a maximum in a blurred partial derivative with respect to an increasing radius r of a normalized contour integral of the image I(x, y) along a circular arc ds of the radius r and coordinates (x0, y0) of a center. The symbol ‘*’ denotes convolution, and Gσ(r) is a smoothing function such as a Gaussian function of standard deviation σ.
The IDO acts as a circular edge detector. The IDO searches for a maximum of a gradient over a 3D parameter space. Therefore, there is no need to use a threshold as in a conventional Canny edge detector, Canny, J., “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-698, 1986.
Another method uses a Hough transform, Wildes, R., “Iris recognition: An emerging biometric technology,” Proc. IEEE 85, pp. 1348-1363, 1997. That method detects edges in iris images followed by a circular Hough transform to localize iris boundaries. The Hough transform searches the optimum parameters of the following optimization,
                                                        max                              (                                  r                  ,                                      x                    0                                    ,                                      y                    0                                                  )                                      ⁢                                          ∑                                  j                  =                  1                                n                            ⁢                              h                ⁡                                  (                                                            x                      j                                        ,                                          y                      j                                        ,                                          x                      0                                        ,                                          y                      0                                        ,                    r                                    )                                                              ,                                          ⁢          where                ⁢                                  ⁢                              h            ⁡                          (                                                x                  j                                ,                                  y                  j                                ,                                  x                  0                                ,                                  y                  0                                ,                r                            )                                =                      {                                                                                                      1                      ,                                                                                                                          if                        ⁢                                                                                                  ⁢                                                  g                          ⁡                                                      (                                                                                          x                                j                                                            ,                                                              y                                j                                                            ,                                                              x                                0                                                            ,                                                              y                                0                                                            ,                              r                                                        )                                                                                              =                      0                                                                                                                                  0                      ,                                                                            otherwise                                                              ,                                                          (        2        )            withg(xj, yj, x0, y0, r)=(xj−x0)2+(yj−y0)2−r2,for edge pixels(xj, yj), j=1, . . . , n. 
One problem of the edge detection and Hough transform methods is the use of thresholds during edge detection. Different threshold values can result in different edges. Different thresholds can significantly affect the results of the Hough transform, Proenca, H., Alexandre, L., “Ubiris: A noisy iris image database,” Intern. Confer. on Image Analysis and Processing, 2005.
Most other methods are essentially minor variants of Daugman's IDO or Wildes' combination of edge detection and Hough transform, by either constraining a parameter search range or optimizing the search process. For example, Ma et al. roughly estimate a location of the pupil position using projections and thresholds of pixel intensities. This is followed by Canny edge detection and a circular Hough transform, Ma, L., Tan, T., Wang, Y., Zhang, D. “Personal identification based on iris texture analysis,” IEEE, Trans. on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1519-1533, 2003.
Masek describes an edge detection method slightly different from the Canny detector, and then uses the circular Hough transform for iris boundary extraction, Masek, L., Kovesi, P., “MATLAB Source Code for a Biometric Identification System Based on Iris Patterns,” The School of Computer Science and Software Engineering, The University of Western Australia 2003.
Kim et al. use mixtures of three Gaussian distributions to coarsely segment eye images into dark, intermediate, and bright regions, and then use the Hough transform for iris localization, Kim, J., Cho, S., Choi, J. “Iris recognition using wavelet features,” Journal of VLSI Signal Processing, vol. 38, pp. 147-156, 2004.
Rad et al. use gradient vector pairs at various directions to coarsely estimate positions of a circle and then use Daugman's IDO to refine the iris boundaries, Rad, A., Safabakhsh, R., Qaragozlou, N., Zaheri, M. “Fast iris and pupil localization and eyelid removal using gradient vector pairs and certainty factors,” The Irish Machine Vision and Image Processing Conf., pp. 82-91, 2004.
Cui et al. determine a wavelet transform and then use the Hough transform to locate the inner boundary of the iris, while using Daugman's IDO for the outer boundary, Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z., “A fast and robust iris localization method based on texture segmentation,” Proc. SPIE on Biometric Technology for Human Identification, vol. 5404, pp. 401-408, 2004.
None of the above methods use texture in the image for iris boundary extraction. In the method of Cui et al., texture is only used to roughly define an area in the image that is partially occluded by eyelashes and eyelids. A parabolic arc is fit to an eyelid within the area to generate a mask using Daugman's IDO.
Because of possible eyelid occlusions, eyelids can be removed using a mask image, Daugman, J., “How iris recognition works,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, pp. 21-30, 2004. Typical techniques detect eyelid boundaries in the images of the eye.
Daugman uses arcuate curves with spline fitting to explicitly locate eyelid boundaries. As stated above, Cui et al. use a parabolic model for the eyelids. Masek uses straight lines to approximate the boundaries of the eyelids. That results in a larger mask than necessary.
Almost all prior art methods estimate explicitly the eyelid boundaries in the original eye images. That is intuitive but has some problems in practice. The search range for eyelids is usually large, making the search process slow, and most important, the eyelids are always estimated, even when the eyelids do not occlude the iris.
Iris Feature Extraction
Daugman unwraps a circular image into a rectangular image after an iris has been localized using the integro-differential operator. Then, a set of 2D Gabor filters is applied to the unwrapped image to obtain quantized local phase angles for iris feature extraction. The resulting binary feature vector is called the ‘iris code.’ The binary iris code is matched using a Hamming distance.
Wildes describes another iris recognition system where a Laplacian of Gaussian filters are applied for iris feature extraction and the irises are matched with normalized correlation.
Zero-crossings of wavelet transforms at various scales on a set of 1D iris rings have been used for iris feature extraction, Boles, W., Boashash, B., “A Human Identification Technique Using Images of the Iris and Wavelet Transform,” IEEE Trans. On Signal Processing, vol. 46, pp. 1185-1188, 1998.
A 2D wavelet transform was used and quantized to form an 87-bit code, Lim, S., Lee, K., Byeon, O., Kim, T. “Efficient iris recognition through improvement of feature vector and classifier,” ETRI J., vol. 23, pp. 61-70, 2001. However, that method cannot deal with the eye rotation problem, which is common in iris capture.
Masek describes an iris recognition system using a 1D log-Gabor filter for binary iris code extraction. Ma et al. used two circular symmetric filters and computed the mean and standard deviation in small blocks for iris feature extraction with a large feature dimension, Ma, L., Tan, T., Wang, Y., Zhang, D., “Personal identification based on iris texture analysis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1519-1533, 2003. Ma et al. also describes a method based on local variation analysis using a 1D wavelet transform, see also, Ma, L., Tan, T., Wang, Y., Zhang, D. “Efficient iris recognition by characterizing key local variations,” IEEE Trans. on Image Processing, vol. 13, pp. 739-750, 2004.
Another method characterizes a local gradient direction for iris feature extraction, Sun, Z., Tan, T., Wang, Y. “Robust encoding of local ordinal measures: A general framework of iris recognition” ECCV workshop on Biometric Authentication, 2004. That method is computationally complex and results in relatively large feature vectors.
All of the prior art methods for iris feature extraction employ filtering steps that are computationally complex and time-consuming. There is a need for a method of iris feature extraction which can achieve high accuracy for iris matching in biometric identification protocols, and is less complex computationally.